Data migration in a distributive file system

ABSTRACT

A plurality of computing devices are communicatively coupled to each other via a network, and each of the plurality of computing devices is operably coupled to one or more of a plurality of storage devices. A plurality of failure resilient address spaces are distributed across the plurality of storage devices such that each of the plurality of failure resilient address spaces spans a plurality of the storage devices. The plurality of computing devices maintains metadata that maps each failure resilient address space to one of the plurality of computing devices. The metadata is grouped into buckets. Each bucket is stored in the backend of a computing device. Data may be migrated from an external file system to the plurality of storage devices using inode stubs to represent directories and files of the external file system. As the contents of the external file system are copied, the inode stubs are replaced with real inodes.

PRIORITY CLAIM

This application claims priority to the following application, which ishereby incorporated herein by reference:

U.S. provisional patent application 62/691,732 titled “DATA MIGRATION INA DISTRIBUTIVE FILE SYSTEM” filed on Jun. 29, 2018.

BACKGROUND

Limitations and disadvantages of conventional approaches to data storagewill become apparent to one of skill in the art, through comparison ofsuch approaches with some aspects of the present method and system setforth in the remainder of this disclosure with reference to thedrawings.

INCORPORATION BY REFERENCE

U.S. patent application Ser. No. 15/243,519 titled “Distributed ErasureCoded Virtual File System” is hereby incorporated herein by reference inits entirety.

BRIEF SUMMARY

Methods and systems are provided for data migration in a distributedfile system substantially as illustrated by and/or described inconnection with at least one of the figures, as set forth morecompletely in the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates various example configurations of a distributed filesystem in accordance with aspects of this disclosure.

FIG. 2 illustrates an example configuration of a distributed file systemnode in accordance with aspects of this disclosure.

FIG. 3 illustrates another representation of a distributed file systemin accordance with an example implementation of this disclosure.

FIG. 4A illustrates an example of an external file system and aninternal file system in accordance with an example implementation ofthis disclosure.

FIG. 4B illustrates an example of data migration from the external filesystem to the internal file system in accordance with an exampleimplementation of this disclosure.

FIG. 4C illustrates an example of the internal file system followingdata migration in accordance with an example implementation of thisdisclosure.

FIG. 5 is a flowchart illustrating an example method for data migrationto a distributed file system in accordance with this disclosure.

DETAILED DESCRIPTION

Traditionally, file systems use a centralized control over the metadatastructure (e.g., directories, files, attributes, file contents). If alocal file system is accessible from a single server and that serverfails, the file system's data may be lost if as there is no furtherprotection. To add protection, some file systems (e.g., as provided byNetApp) have used one or more pairs of controllers in an active-passivemanner to replicate the metadata across two or more computers. Othersolutions have used multiple metadata servers in a clustered way (e.g.,as provided by IBM GPFS, Dell EMC Isilon, Lustre, etc.). However,because the number of metadata servers in a traditional clustered systemis limited to small numbers, such systems are unable to scale.

The systems in this disclosure are applicable to small clusters and canalso scale to many, many thousands of nodes. An example embodiment isdiscussed regarding non-volatile memory (NVM), for example, flash memorythat comes in the form of a solid-state drive (SSD). The NVM may bedivided into 4 kB blocks and 128 MB chunks. Extents may be stored involatile memory, e.g., RAM for fast access, backed up by NVM storage aswell. An extent may store pointers for blocks, e.g., 256 pointers to 1MB of data stored in blocks. In other embodiments, larger or smallermemory divisions may also be used. Metadata functionality in thisdisclosure may be effectively spread across many servers. For example,in cases of “hot spots” where a large load is targeted at a specificportion of the file system's namespace, this load can be distributedacross a plurality of nodes.

FIG. 1 illustrates various example configurations of a distributed filesystem in accordance with aspects of this disclosure. Shown in FIG. 1 isa local area network (LAN) 102 comprising one or more nodes 120 (indexedby integers from 1 to J, for j≥1), and optionally comprising (indicatedby dashed lines): one or more dedicated storage nodes 106 (indexed byintegers from 1 to M, for M≥1), one or more compute nodes 104 (indexedby integers from 1 to N, for N≥1), and/or an edge router that connectsthe LAN 102 to a remote network 118. The remote network 118 optionallycomprises one or more storage services 114 (indexed by integers from 1to K, for K≥1), and/or one or more dedicated storage nodes 115 (indexedby integers from 1 to L, for L≥1).

Each node 120 _(j) (j an integer, where 1≤j≤J) is a networked computingdevice (e.g., a server, personal computer, or the like) that comprisescircuitry for running processes (e.g., client processes) either directlyon an operating system of the device 104 _(n) and/or in one or morevirtual machines running in the device 104 _(n).

The compute nodes 104 are networked devices that may run a virtualfrontend without a virtual backend. A compute node 104 may run a virtualfrontend by taking a single root input/output virtualization (SR-IOV)into the network interface card (NIC) and consuming a complete processorcore. Alternatively, the compute node 104 may run the virtual frontendby routing the networking through a Linux kernel networking stack andusing kernel process scheduling, thus not having the requirement of afull core. This is useful if a user does not want to allocate a completecore for the file system or if the networking hardware is incompatiblewith the file system requirements.

FIG. 2 illustrates an example configuration of a node in accordance withaspects of this disclosure. A node comprises a frontend 202 and driver208, a memory controller 204, a backend 206, and an SSD agent 214. Thefrontend 202 may be a virtual frontend; the memory controller 204 may bea virtual memory controller; the backend 206 may be a virtual backend;and the driver 208 may be a virtual drivers. As used in this disclosure,a virtual file system (VFS) process is a process that implements one ormore of: the frontend 202, the memory controller 204, the backend 206,and the SSD agent 214. Thus, in an example implementation, resources(e.g., processing and memory resources) of the node may be shared amongclient processes and VFS processes. The processes of the VFS may beconfigured to demand relatively small amounts of the resources tominimize the impact on the performance of the client applications. Thefrontend 202, the memory controller 204, and/or the backend 206 and/orthe SSD agent 214 may run on a processor of the host 201 or on aprocessor of the network adaptor 218. For a multi-core processor,different VFS process may run on different cores, and may run adifferent subset of the services. From the perspective of the clientprocess(es) 212, the interface with the virtual file system isindependent of the particular physical machine(s) on which the VFSprocess(es) are running. Client processes only require driver 208 andfrontend 202 to be present in order to serve them.

The node may be implemented as a single tenant server (e.g., bare-metal)running directly on an operating system or as a virtual machine (VM)and/or container (e.g., a Linux container (LXC)) within a bare-metalserver. The VFS may run within an LXC container as a VM environment.Thus, inside the VM, the only thing that may run is the LXC containercomprising the VFS. In a classic bare-metal environment, there areuser-space applications and the VFS runs in an LXC container. If theserver is running other containerized applications, the VFS may runinside an LXC container that is outside the management scope of thecontainer deployment environment (e.g. Docker).

The node may be serviced by an operating system and/or a virtual machinemonitor (VMM) (e.g., a hypervisor). The VMM may be used to create andrun the node on a host 201. Multiple cores may reside inside the singleLXC container running the VFS, and the VFS may run on a single host 201using a single Linux kernel. Therefore, a single host 201 may comprisemultiple frontends 202, multiple memory controllers 204, multiplebackends 206, and/or one or more drivers 208. A driver 208 may run inkernel space outside the scope of the LXC container.

A SR-IOV PCIe virtual function may be used to run the networking stack210 in user space 222. SR-IOV allows the isolation of PCI Express, suchthat a single physical PCI Express can be shared on a virtualenvironment and different virtual functions may be offered to differentvirtual components on a single physical server machine. The I/O stack210 enables the VFS node to bypasses the standard TCP/IP stack 220 andcommunicate directly with the network adapter 218. A Portable OperatingSystem Interface for uniX (POSIX) VFS functionality may be providedthrough lockless queues to the VFS driver 208. SR-IOV or full PCIephysical function address may also be used to run non-volatile memoryexpress (NVMe) driver 214 in user space 222, thus bypassing the Linux IOstack completely. NVMe may be used to access non-volatile storage device216 attached via a PCI Express (PCIe) bus. The non-volatile storagedevice 220 may be, for example, flash memory that comes in the form of asolid-state drive (SSD) or Storage Class Memory (SCM) that may come inthe form of an SSD or a memory module (DIMM). Other example may includestorage class memory technologies such as 3D-XPoint.

The SSD may be implemented as a networked device by coupling thephysical SSD 216 with the SSD agent 214 and networking 210.Alternatively, the SSD may be implemented as a network-attached NVMe SSD242 or 244 by using a network protocol such as NVMe-oF (NVMe overFabrics). NVMe-oF may allow access to the NVMe device using redundantnetwork links, thereby providing a higher level or resiliency. Networkadapters 226, 228, 230 and 232 may comprise hardware acceleration forconnection to the NVMe SSD 242 and 244 to transform them into networkedNVMe-oF devices without the use of a server. The NVMe SSDs 242 and 244may each comprise two physical ports, and all the data may be accessedthrough either of these ports.

Each client process/application 212 may run directly on an operatingsystem or may run in a virtual machine and/or container serviced by theoperating system and/or hypervisor. A client process 212 may read datafrom storage and/or write data to storage in the course of performingits primary function. The primary function of a client process 212,however, is not storage-related (i.e., the process is only concernedthat its data is reliably stored and is retrievable when needed, and notconcerned with where, when, or how the data is stored). Exampleapplications which give rise to such processes include: email servers,web servers, office productivity applications, customer relationshipmanagement (CRM), animated video rendering, genomics calculation, chipdesign, software builds, and enterprise resource planning (ERP).

A client application 212 may make a system call to the kernel 224 whichcommunicates with the VFS driver 208. The VFS driver 208 puts acorresponding request on a queue of the VFS frontend 202. If several VFSfrontends exist, the driver may load balance accesses to the differentfrontends, making sure a single file/directory is always accessed viathe same frontend. This may be done by sharding the frontend based onthe ID of the file or directory. The VFS frontend 202 provides aninterface for routing file system requests to an appropriate VFS backendbased on the bucket that is responsible for that operation. Theappropriate VFS backend may be on the same host or it may be on anotherhost.

A VFS backend 206 hosts several buckets, each one of them services thefile system requests that it receives and carries out tasks to otherwisemanage the virtual file system (e.g., load balancing, journaling,maintaining metadata, caching, moving of data between tiers, removingstale data, correcting corrupted data, etc.)

A VFS SSD agent 214 handles interactions with a respective storagedevice 216. This may include, for example, translating addresses, andgenerating the commands that are issued to the storage device (e.g., ona SATA, SAS, PCIe, or other suitable bus). Thus, the VFS SSD agent 214operates as an intermediary between a storage device 216 and the VFSbackend 206 of the virtual file system. The SSD agent 214 could alsocommunicate with a standard network storage device supporting a standardprotocol such as NVMe-oF (NVMe over Fabrics).

FIG. 3 illustrates another representation of a distributed file systemin accordance with an example implementation of this disclosure. In FIG.3, the element 302 represents memory resources (e.g., DRAM and/or othershort-term memory) and processing (e.g., x86 processor(s), ARMprocessor(s), NICs, ASICs, FPGAs, and/or the like) resources of variousnode(s) (compute, storage, and/or VFS) on which resides a virtual filesystem, such as described regarding FIG. 2 above. The element 308represents the one or more physical storage devices 216 which providethe long term storage of the virtual file system.

As shown in FIG. 3, the physical storage is organized into a pluralityof distributed failure resilient address spaces (DFRASs) 318. Each ofwhich comprises a plurality of chunks 310, which in turn comprises aplurality of blocks 312. The organization of blocks 312 into chunks 310is only a convenience in some implementations and may not be done in allimplementations. Each block 312 stores committed data 316 (which maytake on various states, discussed below) and/or metadata 314 thatdescribes or references committed data 316.

The organization of the storage 308 into a plurality of DFRASs enableshigh performance parallel commits from many—perhaps all—of the nodes ofthe virtual file system (e.g., all nodes 104 ₁-104 _(N), 106 ₁-106 _(M),and 120 ₁-120 _(J) of FIG. 1 may perform concurrent commits inparallel). In an example implementation, each of the nodes of thevirtual file system may own a respective one or more of the plurality ofDFRAS and have exclusive read/commit access to the DFRASs that it owns.

Each bucket owns a DFRAS, and thus does not need to coordinate with anyother node when writing to it. Each bucket may build stripes across manydifferent chunks on many different SSDs, thus each bucket with its DFRAScan choose what “chunk stripe” to write to currently based on manyparameters, and there is no coordination required in order to do so oncethe chunks are allocated to that bucket. All buckets can effectivelywrite to all SSDs without any need to coordinate.

Each DFRAS being owned and accessible by only its owner bucket that runson a specific node allows each of the nodes of the VFS to control aportion of the storage 308 without having to coordinate with any othernodes (except during [re]assignment of the buckets holding the DFRASsduring initialization or after a node failure, for example, which may beperformed asynchronously to actual reads/commits to storage 308). Thus,in such an implementation, each node may read/commit to its buckets'DFRASs independently of what the other nodes are doing, with norequirement to reach any consensus when reading and committing tostorage 308. Furthermore, in the event of a failure of a particularnode, the fact the particular node owns a plurality of buckets permitsmore intelligent and efficient redistribution of its workload to othernodes (rather the whole workload having to be assigned to a single node,which may create a “hot spot”). In this regard, in some implementationsthe number of buckets may be large relative to the number of nodes inthe system such that any one bucket may be a relatively small load toplace on another node. This permits fine grained redistribution of theload of a failed node according to the capabilities and capacity of theother nodes (e.g., nodes with more capabilities and capacity may begiven a higher percentage of the failed nodes buckets).

To permit such operation, metadata may be maintained that maps eachbucket to its current owning node such that reads and commits to storage308 can be redirected to the appropriate node.

Load distribution is possible because the entire file system metadataspace (e.g., directory, file attributes, content range in the file,etc.) can be broken (e.g., chopped or sharded) into small, uniformpieces (e.g., “shards”). For example, a large system with 30k serverscould chop the metadata space into 128k or 256k shards.

Each such metadata shard may be maintained in a “bucket.” Each VFS nodemay have responsibility over several buckets. When a bucket is servingmetadata shards on a given backend, the bucket is considered “active” orthe “leader” of that bucket. Typically, there are many more buckets thanVFS nodes. For example, a small system with 6 nodes could have 120buckets, and a larger system with 1,000 nodes could have 8k buckets.

Each bucket may be active on a small set of nodes, typically 5 nodesthat that form a penta-group for that bucket. The cluster configurationkeeps all participating nodes up-to-date regarding the penta-groupassignment for each bucket.

Each penta-group monitors itself. For example, if the cluster has 10kservers, and each server has 6 buckets, each server will only need totalk with 30 different servers to maintain the status of its buckets (6buckets will have 6 penta-groups, so 6*5=30). This is a much smallernumber than if a centralized entity had to monitor all nodes and keep acluster-wide state. The use of penta-groups allows performance to scalewith bigger clusters, as nodes do not perform more work when the clustersize increases. This could pose a disadvantage that in a “dumb” mode asmall cluster could actually generate more communication than there arephysical nodes, but this disadvantage is overcome by sending just asingle heartbeat between two servers with all the buckets they share (asthe cluster grows this will change to just one bucket, but if you have asmall 5 server cluster then it will just include all the buckets in allmessages and each server will just talk with the other 4). Thepenta-groups may decide (i.e., reach consensus) using an algorithm thatresembles the Raft consensus algorithm.

Each bucket may have a group of compute nodes that can run it. Forexample, five VFS nodes can run one bucket. However, only one of thenodes in the group is the controller/leader at any given moment.Further, no two buckets share the same group, for large enough clusters.If there are only 5 or 6 nodes in the cluster, most buckets may sharebackends. In a reasonably large cluster there many distinct node groups.For example, with 26 nodes, there are more than 64,000

$\left( \frac{2{6!}}{5{!{*{\left( {{26} - 5} \right)!}}}} \right)$

possible five-node groups (i.e., penta-groups).

All nodes in a group know and agree (i.e., reach consensus) on whichnode is the actual active controller (i.e., leader) of that bucket. Anode accessing the bucket may remember (“cache”) the last node that wasthe leader for that bucket out of the (e.g., five) members of a group.If it accesses the bucket leader, the bucket leader performs therequested operation. If it accesses a node that is not the currentleader, that node indicates the leader to “redirect” the access. Ifthere is a timeout accessing the cached leader node, the contacting nodemay try a different node of the same penta-group. All the nodes in thecluster share common “configuration” of the cluster, which allows thenodes to know which server may run each bucket.

Each bucket may have a load/usage value that indicates how heavily thebucket is being used by applications running on the file system. Forexample, a server node with 11 lightly used buckets may receive anotherbucket of metadata to run before a server with 9 heavily used buckets,even though there will be an imbalance in the number of buckets used.Load value may be determined according to average response latencies,number of concurrently run operations, memory consumed or other metrics.

Redistribution may also occur even when a VFS node does not fail. If thesystem identifies that one node is busier than the others based on thetracked load metrics, the system can move (i.e., “fail over”) one of itsbuckets to another server that is less busy. However, before actuallyrelocating a bucket to a different host, load balancing may be achievedby diverting writes and reads. Since each write may end up on adifferent group of nodes, decided by the DFRAS, a node with a higherload may not be selected to be in a stripe to which data is beingwritten. The system may also opt to not serve reads from a highly loadednode. For example, a “degraded mode read” may be performed, wherein ablock in the highly loaded node is reconstructed from the other blocksof the same stripe. A degraded mode read is a read that is performed viathe rest of the nodes in the same stripe, and the data is reconstructedvia the failure protection. A degraded mode read may be performed whenthe read latency is too high, as the initiator of the read may assumethat that node is down. If the load is high enough to create higher readlatencies, the cluster may revert to reading that data from the othernodes and reconstructing the needed data using the degraded mode read.

Each bucket manages its own distributed erasure coding instance (i.e.,DFRAS 518) and does not need to cooperate with other buckets to performread or write operations. There are potentially thousands of concurrent,distributed erasure coding instances working concurrently, each for thedifferent bucket. This is an integral part of scaling performance, as iteffectively allows any large file system to be divided into independentpieces that do not need to be coordinated, thus providing highperformance regardless of the scale.

Each bucket handles all the file systems operations that fall into itsshard. For example, the directory structure, file attributes and filedata ranges will fall into a particular bucket's jurisdiction.

An operation done from any frontend starts by finding out what bucketowns that operation. Then the backend leader, and the node, for thatbucket is determined. This determination may be performed by trying thelast-known leader. If the last-known leader is not the current leader,that node may know which node is the current leader. If the last-knownleader is not part of the bucket's penta-group anymore, that backendwill let the front end know that it should go back to the configurationto find a member of the bucket's penta-group. The distribution ofoperations allows complex operations to be handled by a plurality ofservers, rather than by a single computer in a standard system.

If the cluster of size is small (e.g., 5) and penta-groups are used,there will be buckets that share the same group. As the cluster sizegrows, buckets are redistributed such that no two groups are identical.

FIG. 4A illustrates an example of a cluster 427 and an external filesystem 421 in accordance with an example implementation of thisdisclosure. The cluster 427 comprises an internal file system that mayreside on one or more network interface cards (NICs). The external filesystem 421 comprises an external client 423 and external storage.

For illustration, the cluster 427, as illustrated, comprises twocomputing devices 403 and 413 and four storage devices 409 a, 409 b, 409c and 409 d. A different number of computing devices (e.g., CPU basedservers) and storage devices (e.g., SSDs) may be used without deviatingfrom this disclosure. Computing devices 403 and 413 and storage devices409 a, 409 b, 409 c and 409 d may be operably coupled via one or moreNICs. For example, each computing device 403 and 413 may be on aseparate NIC, and the storage devices 409 a, 409 b, 409 c and 409 d maybe on one or more NICs.

Computing device 401 comprises a frontend 403 and a backend 405. Thebackend 405 comprises at least one bucket 407. Computing device 411comprises a frontend 413 and a backend 415. The backend 415 comprises atleast one bucket 417.

Each bucket in a backend is operable to build one or more failureresilient stripes comprising a plurality of blocks. For example, with 10blocks, 8 blocks of data could be protected with 2 blocks of errorprotection/correction (i.e., using an 8+2 stripe). Likewise, with 10failure domains, 6 blocks of data could be protected with 4 blocks oferror protection/correction (i.e., using a 6+4 stripe).

Bucket 407 is operable to build failure-resilient stripe 419, whichcomprises block a1, block a2 and block a3. In general, each storageblock of the plurality of storage blocks in a particularfailure-resilient (or failure-protected) stripe may be located in adifferent storage device of the plurality of storage devices.Alternatively, the plurality of storage blocks of a failure-resilientstripe may be distributed across at least two storage devices of theplurality of storage devices.

Bucket 407 may generate, manage and control failure-resilient stripe 419via an inode and one or more extents. The inode is a data structure onthat stores all the information about a file or directory except itsname and its actual data. Each 4k block in a file may be handled by anobject called an extent. The extent ID may be based on the inode ID andan offset (e.g., 1 MB) within the file. And, the extent ID is managed bya bucket. The extents may be stored in the registry of each bucket. Theextent ranges may be protected by read-write locks. Each extent maymanage the content of a contiguous 1 MB for a file, by managing thepointers of up to 256 4k blocks that are responsible for that 1 MB data.In other embodiments, larger or smaller memory divisions may also beused. Extents may be stored in volatile memory, e.g., RAM for fastaccess, backed up by NVM storage as well.

FIG. 4B illustrates an example of a data migration from the externalfile system 421 to the internal file system in cluster 427 in accordancewith an example implementation of this disclosure. Migrating large filesystems could be a very lengthy process, especially if a user tiersmultiple file systems. Furthermore, a user may also require access tothe external file system before migration can be completed.

The external file system 421 may or may not change during the migration.The migration includes a mapping process to go over the namespace of theexternal storage 425 and create a directory hierarchy with stub inodes(e.g., stub inodes 431). The stub inode 431 is a substitute for anactual inode, which couples to the storage 409 a-d via one or moreextents 433. The stub inode 431 may quickly allow access to thepreexisting external storage 425, while data is migrated, in thebackground, to the storage 409 a-d. The mapping process may generateinode stubs for directories as well as files.

The user may be notified when the entire migration process is complete.Upon completion, all stub inodes will be replaced by actual inodes.Alternatively, each stub inode will be replaced by an actual inode asthe data is migrated. An opendir function may be run to block an accessoperation to files on a particular directory until the backgroundprocess completes the entire directory migration. For directories,opendir may block access until all enclosed file stubs and directorystubs are generated. Thereafter, the opendir will return and readdir maystart.

When a file inode stub (or directory inode stub) is opened, the systemmay pause until that file (or directory) is pulled from the filer. Whenfile inode stubs are open, the migration may create all extents that arepossible according to the associated external file. These extents 433are associated with locations in an extent stripe 437.

A background process may go through the queue of all the stubs andcontinue creating the namespace 429 and coping the files. The backgroundprocess may go through all directory inode stubs and then go through allfile inode stubs. After all (or each) of the file inodes are created,they may be converted to real inodes and the contents of external filesmay be downloaded to the extents.

The system may maintain the state of the migration and may be able tonotify a user when all files have been copied to the cluster system 427.Each migration process may need to know whether the correspondingnamespace 429 still has some stubs (either inodes or extents) or whetherall stubs have been converted.

Even if the external file system 421 is large, the data migration isresilient to restarts and failures. The root of the namespace 429 maynot be accessed until all files of the root directory are migrated.

Data migration may run concurrently from several nodes. Each backendwill be responsible for its own directories and files. If one computingdevice node 411 is unable to directly mount the migrated external filesystem 421 and another computing device node 401 is able to directlymount the migrated external file system 421, a tunnel 435 may begenerated from the backend 415 of one computing device node 411 to thebackend 405 of another computing device node 401.

During migration, progress (e.g., in terms of the number or capacity offiles moved) may be indicated. The state of the extents to be fetchedand inode stubs to be converted to real directories/files may bemaintained. The migration may continue running throughout bucketfailover and system restarts.

FIG. 4C illustrates an example of the internal file system of cluster427 following data migration in accordance with an exampleimplementation of this disclosure. In the example of FIG. 4C, stripe 437has been populated by block b1 and block b2. An external file wasmigrated to block b1. Block b2 comprises data that may be used torestore block b1. The sparse file indicates that the external filesystem required less space than was allocated. If upon reading anexternal file, an extent reads full 0s, the extent will be deleted tocreate a sparse file. The extent corresponding to the sparse file may beadded to a queue via a tag in the registry.

FIG. 5 is a flowchart illustrating an example method for data migrationfrom an external file system to an internal distributed file system inaccordance with this disclosure. In block 501, inode stubs are generatedfor the files and directories of an external file system. For example, abucket in a computing device backend may generate these stub inode datastructures.

In block 503, one or more stripes are allocated. These stripes span aplurality of storage devices in the internal file system, and eachstripe comprise a plurality of storage blocks. Each stripe may begenerated by a bucket in a computing device backend.

In block 505, the contents of the files and directories of the externalfile system are migrated into the storage blocks of the stripes. Duringthe migration, data may be accessed from the external storage via a stubinode. While a particular file is migrated, access may be blocked by anopen command.

In block 507, one or more blocks of error correction data are generatedfor each stripe according to the migrated data. This error correctiondata is stored in the corresponding stripe. During a degraded read, inwhich data on one or more storage devices is unavailable, the errorcorrection data is used in combination with available data to regeneratethe unavailable data. A storage block of the plurality of storage blocksis designated as a sparse file is it is not used for migrated data orassociated error correction data.

While the present method and/or system has been described with referenceto certain implementations, it will be understood by those skilled inthe art that various changes may be made and equivalents may besubstituted without departing from the scope of the present methodand/or system. In addition, many modifications may be made to adapt aparticular situation or material to the teachings of the presentdisclosure without departing from its scope. Therefore, it is intendedthat the present method and/or system not be limited to the particularimplementations disclosed, but that the present method and/or systemwill include all implementations falling within the scope of theappended claims.

As utilized herein the terms “circuits” and “circuitry” refer tophysical electronic components (i.e. hardware) and any software and/orfirmware (“code”) which may configure the hardware, be executed by thehardware, and or otherwise be associated with the hardware. As usedherein, for example, a particular processor and memory may comprisefirst “circuitry” when executing a first one or more lines of code andmay comprise second “circuitry” when executing a second one or morelines of code. As utilized herein, “and/or” means any one or more of theitems in the list joined by “and/or”. As an example, “x and/or y” meansany element of the three-element set {(x), (y), (x, y)}. In other words,“x and/or y” means “one or both of x and y”. As another example, “x, y,and/or z” means any element of the seven-element set {(x), (y), (z), (x,y), (x, z), (y, z), (x, y, z)}. In other words, “x, y and/or z” means“one or more of x, y and z”. As utilized herein, the term “exemplary”means serving as a non-limiting example, instance, or illustration. Asutilized herein, the terms “e.g.” and “for example” set off lists of oneor more non-limiting examples, instances, or illustrations. As utilizedherein, circuitry is “operable” to perform a function whenever thecircuitry comprises the necessary hardware and code (if any isnecessary) to perform the function, regardless of whether performance ofthe function is disabled or not enabled (e.g., by a user-configurablesetting, factory trim, etc.).

What is claimed is: 1-20. (canceled)
 21. A system comprising: aplurality of storage devices; and a computing device comprising abucket, wherein: the bucket is operable to generate a first datastructure and a second data structure, the first data structure isoperable to access data in an external storage, the second datastructure is operable to write data to a failure-protected stripe, thefailure-protected stripe comprises a plurality of storage blocksdistributed across a group of storage devices selected from theplurality of storage devices, and the plurality of storage blocks isdetermined before data is accessed from the external storage and writtento the plurality of storage devices.
 22. The system of claim 21, whereinthe storage blocks of the failure-protected stripe are distributedacross at least two storage devices of the plurality of storage devices.23. The system of claim 21, wherein at least one storage block of theplurality of storage blocks of the failure-protected stripe is reservedfor error correction data that is associated with the other storageblocks of the plurality of storage blocks of the failure-protectedstripe.
 24. The system of claim 21, wherein the second data structurecomprises an inode and one or more extents that point to the pluralityof storage blocks of the failure-protected stripe.
 25. The system ofclaim 21, wherein the first data structure type is used when data iscopied from the external storage to the plurality of storage devices.26. The system of claim 21, wherein the second data structure is usedafter data has been copied from the external storage to the plurality ofstorage devices.
 27. The system of claim 21, wherein a storage block ofthe plurality of storage blocks is designated as a sparse file if thestorage block is not used for migrated data or associated errorcorrection data.
 28. The system of claim 21, wherein the first datastructure comprises a stub inode.
 29. The system of claim 28, whereinthe stub inode corresponds to a file in the external storage.
 30. Thesystem of claim 28, wherein the stub inode corresponds to a directory inthe external storage.
 31. The system of claim 21, wherein the systemcomprises a network interface card (NIC).
 32. A method comprising:generating, by a bucket, a first data structure comprises a stub inode;designating, by the bucket, a stripe comprising a plurality of storageblocks across a group of storage devices selected from a plurality ofstorage devices; migrating data from an external storage to the stripe,wherein the plurality of storage blocks in the plurality of storagedevices are determined before data is migrated from the external storageto the plurality of storage devices; accessing data from the externalstorage during data migration, via the stub inode in the first datastructure; and accessing data from the stripe after data migration, viaa second data structure.
 33. The method of claim 32, wherein theplurality of storage blocks of the stripe are distributed across atleast two storage devices of the plurality of storage devices.
 34. Themethod of claim 32, wherein at least one storage block of the pluralityof storage blocks of the stripe is reserved for a block of errorcorrection data that is generated according to the other storage blocksof the plurality of storage blocks of the stripe.
 35. The method ofclaim 32, wherein the second data structure comprises an inode and oneor more extents that point to the plurality of storage blocks of thestripe.
 36. The method of claim 32, wherein a storage block of theplurality of storage blocks is designated as a sparse file if thestorage block is not used for migrated data or associated errorcorrection data.
 37. The method of claim 32, wherein the stub inodecorresponds to a file in the external storage.
 38. The method of claim32, wherein the stub Mode corresponds to a directory in the externalstorage.
 39. The method of claim 32, wherein the bucket is on a networkinterface card (NIC).
 40. The method of claim 32, wherein the stripe isa failure-protected stripe.